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Wyszukujesz frazę "Lin, C.-H." wg kryterium: Autor


Wyświetlanie 1-2 z 2
Tytuł:
Common driving notification protocol based on classified driving behavior for cooperation intelligent autonomous vehicle using vehicular ad-hoc network technology
Autorzy:
Lin,, C.-H.
Dong, F. -Y.
Hirota, K.
Powiązania:
https://bibliotekanauki.pl/articles/91784.pdf
Data publikacji:
2015
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
common driving notification protocol
CDNP
intelligent autonomous vehicle
identification
simulation tools
NS-3
SUMO
data packet transmission
vehicle mobility
inteligentny pojazd autonomiczny
identyfikacja
narzędzia symulacyjne
transmisja danych pakietowych
mobilność pojazdu
Opis:
A protocol, called common driving notification protocol (CDNP), is proposed based on the classified driving behavior for intelligent autonomous vehicles, and it defines a standard with common messages and format for vehicles. The common standard format and definitions of CDNP packet make the autonomous vehicles have a common language to exchange more detail driving decision information of various driving situations, decrease the identification time for one vehicle to identify the driving decisions of other vehicles before or after those driving decisions are performed. The simulation tools, including NS- 3 and SUMO, are used to simulate the wireless data packet transmission and the vehicle mobility; the experiment results present that the proposed protocol, CDNP, can increase the reaction preparing time with maximum value 250 seconds, decrease the identification time and the average travel time. Prospectively, it is decided to implement the CDNP as a protocol stack in the Linux kernel to provide the basic protocol capability for real world transmission testing.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2015, 5, 1; 5-21
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Genetic algorithm combined with a local search method for identifying susceptibility genes
Autorzy:
Yang, C -H.
Moi, S. -H.
Lin, Y. -D.
Chuang, L. -Y.
Powiązania:
https://bibliotekanauki.pl/articles/91586.pdf
Data publikacji:
2016
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
genetic algorithms
identifying susceptibility genes
local search algorithm
Opis:
Detecting genetic association models between single nucleotide polymorphisms (SNPs) in various disease-related genes can help to understand susceptibility to disease. Statistical tools have been widely used to detect significant genetic association models, according to their related statistical values, including odds ratio (OR), chi-square test (χ2), p-value, etc. However, the high number of computations entailed in such operations may limit the capacity of such statistical tools to detect high-order genetic associations. In this study, we propose lsGA algorithm, a genetic algorithm based on local search method, to detect significant genetic association models amongst large numbers of SNP combinations. We used two disease models to simulate the large data sets considering the minor allele frequency (MAF), number of SNPs, and number of samples. The three-order epistasis models were evaluated by chi-square test (χ2) to evaluate the significance (P-value < 0.05). Analysis results showed that lsGA provided higher chi-square test values than that of GA. Simple linear regression indicated that lsGA provides a significant advantage over GA, providing the highest β values and significant p-value.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2016, 6, 3; 203-212
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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